Script to reproduce years based on a model trained with a specific year¶
Importing¶
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import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import salishsea_tools.viz_tools as sa_vi
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import root_mean_squared_error as rmse
from tqdm.auto import tqdm
import random
Datasets Preparation¶
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def datasets_preparation(dataset):
drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
np.ravel(dataset['Temperature_(15m-100m)']), np.ravel(dataset['Salinity_(0m-15m)']),
np.ravel(dataset['Salinity_(15m-100m)'])])
indx = np.where(~np.isnan(drivers).any(axis=0))
drivers = drivers[:,indx[0]]
diat = np.ravel(dataset['Diatom'])
diat = diat[indx[0]]
return(drivers, diat, indx)
Regressor¶
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def regressor (inputs, targets):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
X_train, _, y_train, _ = train_test_split(inputs2, targets)
model = MLPRegressor(hidden_layer_sizes=200)
regr = BaggingRegressor(model, n_estimators=12, n_jobs=4).fit(X_train, y_train)
return (regr)
Regressor for Other Years (Annually)¶
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def regressor2 (inputs, targets, variable_name):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs_test = regr.predict(inputs2)
m = scatter_plot(targets, outputs_test, variable_name)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor for Other Years (Daily)¶
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def regressor3 (inputs, targets):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs_test = regr.predict(inputs2)
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs_test, deg=1)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor for Individual Days¶
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def regressor4 (inputs, targets, variable_name):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs = regr.predict(inputs2)
# Post processing
indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
indx2[indx[0]] = outputs
model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))
m = scatter_plot(targets, outputs, variable_name + str(dates[i].date()))
# Preparation of the dataarray
model = xr.DataArray(model,
coords = {'y': diat_i.y, 'x': diat_i.x},
dims = ['y','x'],
attrs=dict( long_name = variable_name + "Concentration",
units="mmol m-2"),)
plotting3(targets, model, diat_i, variable_name)
Printing¶
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def printing (targets, outputs, m):
print ('The amount of data points is', outputs.size)
print ('The slope of the best fitting line is ', np.round(m,3))
print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
print (' The root mean square error is:', rmse(targets,outputs))
Scatter Plot¶
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def scatter_plot(targets, outputs, variable_name):
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs, deg=1)
printing(targets, outputs, m)
fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')
ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)
lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
np.max([ax[0].get_xlim(), ax[0].get_ylim()])]
# plot fitted y = m*x + b
ax[0].axline(xy1=(0, b), slope=m, color='r')
ax[0].set_xlabel('targets')
ax[0].set_ylabel('outputs')
ax[0].set_xlim(lims)
ax[0].set_ylim(lims)
ax[0].set_aspect('equal')
ax[0].plot(lims, lims,linestyle = '--',color = 'k')
h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet',
range=[lims,lims], cmin=0.1, norm='log')
ax[1].plot(lims, lims,linestyle = '--',color = 'k')
# plot fitted y = m*x + b
ax[1].axline(xy1=(0, b), slope=m, color='r')
ax[1].set_xlabel('targets')
ax[1].set_ylabel('outputs')
ax[1].set_aspect('equal')
fig.colorbar(h[3],ax=ax[1], location='bottom')
fig.suptitle(variable_name)
plt.show()
return (m)
Plotting¶
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def plotting (variable, name):
plt.plot(years,variable, marker = '.', linestyle = '')
plt.xlabel('Years')
plt.ylabel(name)
plt.show()
Plotting 2¶
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def plotting2(variable,title):
fig, ax = plt.subplots()
scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)
ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
fig.show()
Plotting 3¶
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def plotting3(targets, model, variable, variable_name):
fig, ax = plt.subplots(2,2, figsize = (10,15))
cmap = plt.get_cmap('cubehelix')
cmap.set_bad('gray')
variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration [percentage]'})
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.35)
sa_vi.set_aspect(ax[0,0])
sa_vi.set_aspect(ax[0,1])
sa_vi.set_aspect(ax[1,0])
ax[0,0].title.set_text(variable_name + ' (targets)')
ax[0,1].title.set_text(variable_name + ' (outputs)')
ax[1,0].title.set_text('targets - outputs')
ax[1,1].axis('off')
fig.suptitle(str(dates[i].date()))
plt.show()
Training with the Selected Year¶
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# Dataset and date
ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_original.nc')
dates = pd.DatetimeIndex(ds['time_counter'].values)
# just an example
year = 2007
dataset = ds.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset)
regr = regressor(drivers, diat)
Other Years (Anually)¶
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years = range (2007,2024)
r_all = []
rms_all = []
slope_all = []
for year in tqdm(range (2007,2024)):
dataset = ds.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset)
r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
r_all.append(r)
rms_all.append(rms)
slope_all.append(m)
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
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/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.486 The correlation coefficient is: 0.875 The root mean square error is: 0.07810872
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404 The slope of the best fitting line is 0.47 The correlation coefficient is: 0.637 The root mean square error is: 0.12396264
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.402 The correlation coefficient is: 0.588 The root mean square error is: 0.16719967
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.474 The correlation coefficient is: 0.499 The root mean square error is: 0.15231907
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.519 The correlation coefficient is: 0.565 The root mean square error is: 0.14611396
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404 The slope of the best fitting line is 0.457 The correlation coefficient is: 0.644 The root mean square error is: 0.12551421
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.44 The correlation coefficient is: 0.59 The root mean square error is: 0.14834361
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.455 The correlation coefficient is: 0.568 The root mean square error is: 0.14273798
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.412 The correlation coefficient is: 0.205 The root mean square error is: 0.2080702
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404 The slope of the best fitting line is 0.417 The correlation coefficient is: 0.443 The root mean square error is: 0.17821921
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.484 The correlation coefficient is: 0.611 The root mean square error is: 0.12873062
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.43 The correlation coefficient is: 0.196 The root mean square error is: 0.1972452
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.419 The correlation coefficient is: 0.291 The root mean square error is: 0.1975733
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404 The slope of the best fitting line is 0.394 The correlation coefficient is: 0.302 The root mean square error is: 0.21968317
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.465 The correlation coefficient is: 0.618 The root mean square error is: 0.14783666
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.495 The correlation coefficient is: 0.566 The root mean square error is: 0.13722642
/tmp/ipykernel_528/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925 The slope of the best fitting line is 0.506 The correlation coefficient is: 0.477 The root mean square error is: 0.16018644
Other Years (Daily)¶
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r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])
for i in tqdm(range (0, len(ds.time_counter))):
dataset = ds.isel(time_counter=i)
drivers, diat, _ = datasets_preparation(dataset)
r, rms, m = regressor3(drivers, diat)
r_all2 = np.append(r_all2,r)
rms_all2 = np.append(rms_all2,rms)
slope_all2 = np.append(slope_all2,m)
plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
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Daily Maps¶
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maps = random.sample(range(0,len(ds.time_counter)),10)
for i in tqdm(maps):
dataset = ds.isel(time_counter=i)
drivers, diat, indx = datasets_preparation(dataset)
diat_i = dataset['Diatom']
regressor4(drivers, diat, 'Diatom ')
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The amount of data points is 46479 The slope of the best fitting line is 0.046 The correlation coefficient is: 0.019 The root mean square error is: 0.195463
The amount of data points is 46479 The slope of the best fitting line is 0.349 The correlation coefficient is: 0.104 The root mean square error is: 0.2161055
The amount of data points is 46479 The slope of the best fitting line is 0.725 The correlation coefficient is: 0.259 The root mean square error is: 0.1806715
The amount of data points is 46479 The slope of the best fitting line is 0.029 The correlation coefficient is: 0.013 The root mean square error is: 0.17164007
The amount of data points is 46479 The slope of the best fitting line is -0.282 The correlation coefficient is: -0.109 The root mean square error is: 0.17115316
The amount of data points is 46479 The slope of the best fitting line is 1.079 The correlation coefficient is: 0.441 The root mean square error is: 0.17788132
The amount of data points is 46479 The slope of the best fitting line is 0.092 The correlation coefficient is: 0.033 The root mean square error is: 0.16951112
The amount of data points is 46479 The slope of the best fitting line is 0.919 The correlation coefficient is: 0.33 The root mean square error is: 0.16843781
The amount of data points is 46479 The slope of the best fitting line is 0.382 The correlation coefficient is: 0.15 The root mean square error is: 0.19157225
The amount of data points is 46479 The slope of the best fitting line is -0.384 The correlation coefficient is: -0.273 The root mean square error is: 0.25859556
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